ArticleFrontiers in neuroscience2025
A pilot study assessing the clinical utility of deep learning-reconstructed 3D-echo-planar-imaging-based quantitative susceptibility mapping in multiple sclerosis.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Fast Submillimeter Whole-Brain TMagnetic resonance in medicine · 2026Article
- Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.European journal of radiology artificial intelligence · 2026Article
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7 authors.
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Abstract
Background: Quantitative susceptibility mapping (QSM) has emerged as a promising paraclinical tool in multiple sclerosis (MS). This retrospective pilot study aims to evaluate whether a recently proposed deep learning-assisted, k-space-operating reconstruction, denoising and super-resolution technique (DLR) applied on 3D-echo-planar-imaging (3DEPI) protocols, has the potential to improve the quality and clinical utility of QSM in MS, at 3T. Secondarily, we assess whether applying DLR vs. a conventional reconstruction (CR) can improve the quality of QSM based on noise-susceptible, fast 3DEPI protocols. Methods: 3T MRI 3DEPI-data were acquired on seven MS patients and offline-reconstructed using CR and DLR. Results: Deep learning reconstruction-3DEPI-based QSM was rated significantly higher for the confidence in identification of the MS-specific biomarkers (hyper/isointense lesions: Conclusion: Our results constitute a first demonstration of the enhanced quality and clinical utility of the DLR-3DEPI-based QSM in MS.
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